Random matching in 2D with exponent 2 for gaussian densities

TL;DR

This paper explicitly solves the random Euclidean matching problem with exponent 2 for Gaussian points in the plane, determining the exact proportionality constant for the average cost as a function of the number of points.

Contribution

We provide an explicit computation of the constant in the asymptotic average cost for Gaussian points, extending previous asymptotic results to exact solutions.

Findings

Average cost proportional to (log N)^2

Explicit constant determined for Gaussian distribution

Method applicable to unbounded domains

Abstract

We solve the Random Euclidean Matching problem with exponent 2 for the Gaussian distribution defined on the plane. Previous works by Ledoux and Talagrand determined the leading behavior of the average cost up to a multiplicative constant. We explicitly determine the constant, showing that the average cost is proportional to (log N)^2, where N is the number of points. Our approach relies on a geometric decomposition allowing an explicit computation of the constant. Our results illustrate the potential for exact solutions of random matching problems for many distributions defined on unbounded domains on the plane.